服务器日志挖掘在电力业务系统功能推荐中的应用

Application of Server Logs Mining to Functional Recommender Service of Electric Power Business Systems

提出了一种基于服务器日志挖掘的电力业务系统功能推荐服务,首先从电力业务系统服务器日志中获取用户日志数据,然后对含有"脏"数据的用户日志数据进行预处理,以适应数据挖掘与处理;接着由待处理的数据计算用户访问兴趣度,并基于改进的K均值聚类算法将用户访问兴趣度数据集划分为多个具有相近兴趣度的用户集合,最终为用户提供功能个性化推荐服务.实验结果证明该方法在实现电力业务系统信息推荐方面具有较好的效果.

This paper proposes a functional recommendation service of the electric power system based on server logs mining. First of all, we get the user log data from server los, and then preprocess those log data with ＂dirty＂ data. Secondly, we calculate interest-measure of each user pairs by the processed data sets, and we divide data set of interest-measure of each user pairs into multiple classes with similar interest-measure based on improved K-means clustering algorithm. Finally, personalized functional recommendation method is provided to each user. The experimental results prove the effectiveness of our method in electric power business system.